Abstract Background: Trauma is common, but we have little ability to predict who will develop post-trauma psychopathology. Consistent challenges to our understanding of the etiology of post-trauma psychopathology include: (1) obtaining unbiased prospective data on risk factors preceding or concurrent with trauma; (2) the inability to model large comprehensive risk structures with traditional null hypothesis testing methods, despite the knowledge that risk factors do not operate in isolation; and (3) the almost universal focus on PTSD outcomes to date, while post-trauma psychopathology likely involves various symptoms spanning multiple disorder categories. The aims of this study are to (1) use data from a large, prospective population trauma cohort to establish multidimensional classes of post-trauma psychopathology which include diagnoses from various theoretically derived categories (e.g., stress diagnoses, mood disorders, personality disorders) and (2) to discover multivariate predictor sets and novel interactions which predict post- trauma psychopathology class membership and class transitions over time. Given the projected sample size we will also be able to examine gender differences in psychopathology and resilience, as well as differences by trauma type. Study Design: This study will make use of national prospective data previously assembled as part of an R21 project (and augmented with additional trauma data and more years of follow-up) to establish a trauma cohort from 1995 ? 2015. Trauma cohort members will have experienced at least one of 10 traumatic events (i.e., fires/explosions, accidents and assaults, poisoning, life-threatening illness/injury, pregnancy-related trauma and sudden family deaths). Extensive pre- trauma and post-trauma data on psychiatric diagnoses, treatment (medication and psychotherapy) and social variables will be included. We will use latent class analyses to characterize multidimensional post-trauma psychopathology outcomes (including the absence of psychopathology) and latent transition analyses to examine changes in class membership over time. Machine learning statistical methods will be applied to the expansive risk factor data to develop multivariate predictor sets for outcome classes and class transitions over time. Bias analyses will be used to assess the impact of various forms of systematic error on our results. Implications: This study fulfills NIMH?s strategic priorities of (1) charting mental illness trajectories to determine when, where, and how to intervene and (2) strengthening the public health impact of NIMH-supported research. Our approach will achieve robust and valid risk profiles of post-trauma psychopathology in the most efficient way possible by using pre- existing prospective data from a full and unselected population. A life course multidimensional approach to trauma research is a critical next step in this field. In future work, psychopathology classes and multivariate predictor sets discovered as part of this study can be replicated and expanded in other populations to examine variations of our findings, and used as the basis for a more detailed exploration of newly discovered pathways to psychopathology risk and resilience following trauma.